Dynamic Spatial–Temporal Graph Neural Network for Cooling Capacity Prediction in HVDC Systems

Hao Sun, Shaosen Li, Jianxiang Huang, Hao Li, Guanxin Jing, Ye Tao and Xincui Tian ()
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Hao Sun: Kunming Bureau of EHV Transmission Company, Kunming 650217, China
Shaosen Li: Kunming Bureau of EHV Transmission Company, Kunming 650217, China
Jianxiang Huang: Kunming Bureau of EHV Transmission Company, Kunming 650217, China
Hao Li: Kunming Bureau of EHV Transmission Company, Kunming 650217, China
Guanxin Jing: Kunming Bureau of EHV Transmission Company, Kunming 650217, China
Ye Tao: Kunming Bureau of EHV Transmission Company, Kunming 650217, China
Xincui Tian: Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China

Energies, 2025, vol. 18, issue 2, 1-15

Abstract: Predicting the cooling capacity of converter valves is crucial for maintaining the stability and efficiency of high-voltage direct current (HVDC) systems. This task involves handling complex, multi-dimensional time-series data with strong inter-variable dependencies and temporal dynamics. Traditional machine learning methods, while effective in static scenarios, struggle to capture these dependencies, and existing deep learning models often lack the ability to jointly model spatial and temporal relationships. To address these challenges, we propose a novel framework that integrates Graph Neural Networks (GNNs) with temporal dynamics. The GNN component captures spatial dependencies by representing the data as a graph, where nodes correspond to system variables, and edges encode their relationships. Temporal dependencies are modeled using temporal convolutional layers and recurrent neural networks (RNNs), enabling the framework to learn both short-term variations and long-term trends. Additionally, a graph attention mechanism dynamically adjusts the importance of variable relationships, improving prediction accuracy and interoperability. The proposed method demonstrates superior performance over traditional machine learning and deep learning baselines on real-world cooling system data. These results validate the effectiveness of the framework for industrial applications such as cooling system monitoring and predictive maintenance.

Keywords: cooling capacity prediction; graph neural networks; temporal dynamics; industrial applications; predictive maintenance (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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